CONVOLUTIONAL NEURAL NETWORKS BASED GNSS SIGNAL CLASSIFICATION USING CORRELATOR-LEVEL MEASUREMENTS
Bibliographic record
Abstract
Abstract. In urban areas, the None-Line-Of-Sight (NLOS) and Multipath (MP) signals are the major issues degrading the GNSS position accuracy. Signal reception type should be identified before correcting the NLOS or MP induced errors. Signal features, i.e., signal strength, change rate of received signal strength, difference between delta pseudo-range and pseudo-range rate, have been explored in signal reception type classification. In this letter, with the aim to improve the signal classification accuracy, we propose a new GNSS NLOS/MP/LOS signals classification method using the correlator-level measurements. Firstly, vector tracking (VT) is employed to generate correlator-level measurements; secondly, a deep learning method, Convolutional Neural Network (CNN), is employed to automatically extract the features and identify the signal reception type, correlators’ outputs calculated at different code phases are employed as the inputs of the CNN. Field test is carried out for assessing the performance of the proposed method, and the CNN method obtains state-of-art performance compared with the K-nearest Neighbors Algorithm (kNN) and Support Vector Machine (SVM) methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".